人类和深度强化学习代理的目标定向导航依赖于基于矢量和基于过渡的策略的适应组合
Denis C L Lan1, Laurence T Hunt1, Christopher Summerfield1
1Department of Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom.
PLoS biology
|July 29, 2025
概括
人类使用灵活的策略组合来导航新环境,结合地标方向 (向量) 和即时位置变化 (状态过渡). 这种自适应导航反映了深度学习模型和大脑系统中的策略.
科学领域:
- 认知神经科学 认知神经科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 现有的导航研究主要侧重于熟悉的环境.
- 现实世界的导航经常发生在陌生的环境中.
- 在新环境中有效地寻找路径需要将里程碑信息与本地过渡整合起来.
研究的目的:
- 研究在全新的环境中人类导航背后的认知和神经战略.
- 将人类的导航策略与人工智能模型开发的导航策略进行比较.
主要方法:
- 在不熟悉的环境中研究人类目标导向导航.
- 利用深度神经网络进行超训练,并进行强化学习,以找到最短的路径.
- 在导航过程中分析了状态过渡和基于矢量策略的部署.
主要成果:
- 人类在新型环境中适应性地采用了状态过渡和基于矢量策略的组合.
- 深度神经网络为状态过渡和矢量策略开发了专门的单元,反映了人类的方法.
- 神经网络单元显示出类似于哺乳动物导航系统的响应模式和表示几何形状.
结论:
- 在新环境中成功导航取决于状态过渡和矢量策略的适应性混合.
- 这些策略得到了大脑内不同的表示模式的支持.
- 研究结果表明,生物系统和人工系统之间的导航策略的演变趋同.
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